Hugging Face Trending Papers August 10, 2026

Beyond the Capability Boundary: Zeroth-Order Optimization for Self-Evolving LLM Agents

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Self-evolving methods improve the capabilities of LLM agents by sampling trajectories from the underlying LLMs and learning from these trajectories. However, these methods struggle to learn beyond the inherent capability boundary of the agents, since the agents cannot sample correct trajectories on difficult examples for further improvements.

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arXiv Machine Learning

Aug 11

Beyond the Capability Boundary: Zeroth-Order Optimization for Self-Evolving LLM Agents

arXiv:2608. 09292v1 Announce Type: new Abstract: Self-evolving methods improve the capabilities of LLM agents by sampling trajectories from the underlying LLMs and learning from these trajectories.

By Bingzhen Liu, Xiaomeng Fan, Yuwei Wu, Zhi Gao, Mingyang Gao, Chuanhao Li, Yunde Jia

llms agents fine-tuning benchmarks

arXiv AI

Jun 4

Scaling Self-Evolving Agents via Parametric Memory

arXiv:2606. 04536v1 Announce Type: new Abstract: Existing memory-augmented LLM agents store past experience exclusively in prompt space, as textual summaries or retrieved passages, while keeping model parameters frozen throughout a rollout.

By Tao Ren, Weiyao Luo, Hui Yang, Rongzhi Zhu, Xiang Huang, Yuchuan Wu, Bingxue Chou, Jieping Ye, Jiafeng Liang, Yongbin Li, Yijie Peng

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Aug 19

Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements

Agentic ESOpt proposes using evolution strategies (ES) instead of reinforcement learning to fine‑tune large language‑model agents for long‑horizon tasks. ES offers model scalability, flexibility, and better long‑horizon credit assignment, enabling full‑parameter optimization with minimal GPU memory. The framework samples parameter perturbations, evaluates agents with rewards, and updates online, achieving notable performance gains on WebArena‑Lite and in test‑time prompt‑parameter co‑evolution.

By Zhi Zheng, Rongsheng Chen, Yunpeng Ba, Zhenkun Wang, Yee Whye Teh, Wee Sun Lee

llms agents reinforcement-learning fine-tuning

arXiv AI

Aug 12

SBCO: Self-Supervised, Verifier-Grounded Harness Optimization For Planning Agents

arXiv:2608. 10157v1 Announce Type: new Abstract: Self-improving agents seek to reduce the human engineering effort behind AI systems by enabling them to evolve and self-improve their performance over time.

By Vivek Kulkarni, Sudipta Paul, Aounon Kumar, Nicholas Tzou, Srinivas Chappidi

agents safety

arXiv Machine Learning

Aug 4

Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training

arXiv:2608. 02391v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive.

By Zhiyuan Wang, Shengcai Liu, Jiahao Wu, Ning Lu, Hui Ouyang, Shaofeng Zhang, Haoze Lv, Ke Tang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 27

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

arXiv:2607. 21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.

By Shujin Wu, Cheng Qian, Xiusi Chen, Heng Ji

llms reinforcement-learning benchmarks

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Beyond the Capability Boundary: Zeroth-Order Optimization for Self-Evolving LLM Agents

Scaling Self-Evolving Agents via Parametric Memory

Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements

SBCO: Self-Supervised, Verifier-Grounded Harness Optimization For Planning Agents

Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning